Predictive biology: modelling, understanding and harnessing microbial complexity
Name
01262020_review.pdf
Description
Accepted version
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648.52 KB
Format
Adobe PDF
Checksum (MD5)
d78b2dd8e74acdf13816097454c3435b
Author(s)
Lopatkin, Allison J.
Collins, James J.
Date Issued
May 2020
Journal
Nature Reviews Microbiology
Publisher
Springer Science and Business Media LLC
Citation
Lopatkin, Allison J. and James J. Collins. "Predictive biology: modelling, understanding and harnessing microbial complexity." Nature Reviews Microbiology 18, 9 (September 2020): 507–520. © 2020 Springer Nature Limited
Version
Author's final manuscript
Abstract
Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Massachusetts Institute of Technology. Department of Biological Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1038/s41579-020-0372-5
